


'''simple docstring'''


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 							__magic_name__	(       snake_case__    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				None
     lowerCamelCase__       						=				BloomTokenizerFast
     lowerCamelCase__       						=				BloomTokenizerFast
     lowerCamelCase__       						=				True
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				'tokenizer_file'
     lowerCamelCase__       						=				{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          super().setUp()
          __A	:       int      =							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"		)
          tokenizer.save_pretrained(self.tmpdirname		)


     def lowerCAmelCase__  (   self			,     **lowerCamelCase		):


          '''simple docstring'''





          kwargs.update(self.special_tokens_map		)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname			,     **__snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.get_rust_tokenizer()

          __A	:       Any      =							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
          __A	:       str      =							[[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

          __A	:       int      =							tokenizer.batch_encode_plus(__snake_case		)["input_ids"]
          self.assertListEqual(__snake_case			,     __snake_case		)

          __A	:       Optional[Any]      =							tokenizer.batch_decode(__snake_case		)
          self.assertListEqual(__snake_case			,     __snake_case		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase=6		):


          '''simple docstring'''





          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    __A	:       Optional[int]      =							self.rust_tokenizer_class.from_pretrained(__snake_case			,     **__snake_case		)
                    # tokenizer_r.pad_token = None # Hotfixing padding = None
                    # Simple input
                    __A	:       Any      =							"This is a simple input"
                    __A	:       int      =							["This is a simple input 1", "This is a simple input 2"]
                    __A	:       Optional[int]      =							("This is a simple input", "This is a pair")
                    __A	:       Any      =							[
                        ("This is a simple input 1", "This is a simple input 2"),
                        ("This is a simple pair 1", "This is a simple pair 2"),
                    ]

                    # Simple input tests
                    try:
                         tokenizer_r.encode(__snake_case			,     max_length=__snake_case		)
                         tokenizer_r.encode_plus(__snake_case			,     max_length=__snake_case		)

                         tokenizer_r.batch_encode_plus(__snake_case			,     max_length=__snake_case		)
                         tokenizer_r.encode(__snake_case			,     max_length=__snake_case		)
                         tokenizer_r.batch_encode_plus(__snake_case			,     max_length=__snake_case		)
                    except ValueError:
                         self.fail("Bloom Tokenizer should be able to deal with padding"		)

                    __A	:       List[Any]      =							None  # Hotfixing padding = None
                    self.assertRaises(__snake_case			,     tokenizer_r.encode			,     __snake_case			,     max_length=__snake_case			,     padding="max_length"		)

                    # Simple input
                    self.assertRaises(__snake_case			,     tokenizer_r.encode_plus			,     __snake_case			,     max_length=__snake_case			,     padding="max_length"		)

                    # Simple input
                    self.assertRaises(
                        __snake_case			,     tokenizer_r.batch_encode_plus			,     __snake_case			,     max_length=__snake_case			,     padding="max_length"			,     )

                    # Pair input
                    self.assertRaises(__snake_case			,     tokenizer_r.encode			,     __snake_case			,     max_length=__snake_case			,     padding="max_length"		)

                    # Pair input
                    self.assertRaises(__snake_case			,     tokenizer_r.encode_plus			,     __snake_case			,     max_length=__snake_case			,     padding="max_length"		)

                    # Pair input
                    self.assertRaises(
                        __snake_case			,     tokenizer_r.batch_encode_plus			,     __snake_case			,     max_length=__snake_case			,     padding="max_length"			,     )


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[Any]      =							self.get_rust_tokenizer()
          __A	:       List[Any]      =							load_dataset("xnli"			,     "all_languages"			,     split="test"			,     streaming=__snake_case		)

          __A	:       int      =							next(iter(__snake_case		)		)["premise"]  # pick up one data
          __A	:       List[str]      =							list(sample_data.values()		)

          __A	:       Optional[Any]      =							list(map(tokenizer.encode			,     __snake_case		)		)
          __A	:       Any      =							[tokenizer.decode(__snake_case			,     clean_up_tokenization_spaces=__snake_case		) for x in output_tokens]
          self.assertListEqual(__snake_case			,     __snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map		)			,     1		)
          self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()		)[0]		)			,     1		)





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




